Process for tunnelized cyclostationary to achieve low-energy spectrum sensing

ABSTRACT

A system and method that uses tunnelizing for analyzing frequency spectrum. The method may include the steps of under sampling an input signal to take samples in one or more tunnels each with a tunnel bandwidth that is equal to or less than a total analysis bandwidth of the input signal; detecting one or more cyclostationary features of the input signal based on the samples; and determining one or more signal types of the one or more cyclostationary features based on the one or more cyclostationary features.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No. 14/761,958 filed Jul. 20, 2015, now U.S. Pat. No. 9,755,869, which claims priority to PCT Application Serial No. US/2014/066180 filed Nov. 18, 2014, which claims the benefit of U.S. Provisional Application Ser. No. 61/905,554 filed Nov. 18, 2013, the contents of which are incorporated herein by reference.

STATEMENT OF GOVERNMENT INTEREST

The invention was made with United States Government support under Contract No. FA8650-11-C-7160 awarded by the Defense Advanced Research Projects Agency (DARPA). The United States Government has certain rights in this invention.

BACKGROUND

1. Field

The present system relates to spectrum sensing and more particularly to spectrum sensing by means of tunnelized cyclostationary processing. The current invention relates generally to apparatus, systems and methods for spectrum sensing. More particularly, the apparatus, systems and methods relate to wireless systems signal detection, signal characterization and more efficient use of spectrum. Specifically, the apparatus, systems and methods provide for low-energy implementation of cyclostationary spectrum sensing algorithms disclosed herein as “Tunnelized Cyclostationary Processing”.

2. Description of Related Art

Spectrum sensing has been widely used for cognitive radio systems. Techniques that exploit second and higher order cyclostationary features to detect and classify signals are highly desirable. However, their pervasive use and hardware implementation consumes a lot of energy. A significant amount of research has been conducted in the area of spectrum sensing. However, the underlying assumption of a majority of these techniques has been the use of energy detection which performs poorly in dynamic interference environments and cannot perform signal classification. Second and higher order cyclostationary based sensing helps in distinguishing between signals and detecting anomalies. However, a majority of these approaches are prohibitively expensive in terms of computation.

SUMMARY

In one aspect, the system is a spectrum sensor with an RF front end and processing unit that comprises under sampling an input signal to take samples in one or more tunnels; detecting one or more cyclostationary features of the input signal based on the samples; and determining one or more signal types of the one or more cyclostationary features based on the one or more cyclostationary features.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

One or more embodiments are set forth in the drawings and in the following description. The appended claims particularly and distinctly point out and set forth the invention.

FIG. 1A is a diagrammatic view of a data collection and processing device or spectrum sensor which may be used to effect the various methods described herein.

FIG. 1B is a diagrammatic view of the Radio Frequency (RF) Front End/Receiver in one embodiment.

FIG. 2 is a graph which illustrates the concept of tunneling.

FIG. 3A is a flow diagram showing signal processing flow for known and unknown signal detection and characterization.

FIG. 3B shows another instantiation of the signal processing flow.

FIG. 3C is a flow diagram showing Stage 3 spectral correlation function estimation for known type signals types using their known or previously estimated cycle frequencies.

FIG. 3D is a diagrammatic view of a channelizer followed by an Energy Detection (ED) stage in one embodiment.

FIG. 3E shows a whitener output used in a Strip Spectral Correlation analyzer (SSCA) algorithm in stage 4 in one embodiment.

FIG. 4A illustrates Non-conjugate and the Conjugate Spectral Correlation Functions (SCF) for the entire analysis bandwidth.

FIG. 4B is a graph showing non-conjugate spectral correlation function processing for known signal types, where the system only tries to detect certain known Cycle Frequencies (CF).

FIG. 4C further elaborates the difference between Basic Tunneling and Dual Tunneling where the Basic Tunneling is used to detect CFs less than or equal to the Tunnel Bandwidth (BW) and Dual Tunneling is used to detect CFs greater than or equal to the Tunnel BW.

FIGS. 5A, 5B, 5C and 5D show graphs illustrating Stage 3 known type signal detection and characterization using “tunneling”.

FIG. 6A shows the concept behind unknown signal detection using blind CF estimation to find the Detected Frequency Interval (DFI).

FIG. 6B shows Stage 4 spectral correlation function estimation for unknown signal types using strip spectral correlation analyzer (SSCA).

FIGS. 7A, 7B and 7C show graphs illustrating Stage 4 and Stage 5 spectral correlation function estimation for unknown signal types using SSCA.

FIG. 8A shows Stage 5 processing using second and higher order cyclostationary processing where Stage 5 has been further broken down into Levels 0 to 3.

FIG. 8B shows the process of further analyzing the DFI by analyzing the principal domain by dividing it into subdiamond regions.

FIG. 8C illustrates that not all the diamond-like regions need to be analyzed for blind classification of the unknown-types of signal.

FIG. 9A shows Stage 5 Level 0 and Level 1 Cycle Frequency Pattern analysis to recognize various modulation formats. The Signal to Noise Ratio (SNR) is 3 dB in this illustration.

FIGS. 9B and 9C show Stage 5 higher order cyclic cumulants (HOCC) for modulation recognition where second order signal processing may not be sufficient.

Similar numbers refer to similar parts throughout the drawings.

DETAILED DESCRIPTION

A data collection and processing device or spectrum sensor is shown generally at 1 in FIG. 1 and is in a broad sense configured to sample, detect and classify radio frequency signals. Device 1 may include a radio frequency (RF) front end/receiver 2 in communication with one or more antennae 4, a signal processing unit 6 in communication with front end/receiver 2, and a display 8 in communication with signal processing unit 6. Device/sensor 1 may also include or be in communication with another system 10, which may be in communication with the signal processing unit 6. It will be understood by one skilled in the art that device/sensor 1 may be configured in any suitable form capable of effecting the various methods described herein.

RF front end/receiver 2 essentially provides a receiver function and is capable of tuning to a certain frequency band and collecting the signal over a certain desired bandwidth. Front end/receiver 2 may include an analog to digital (A/D) converter for converting the analog signal to a digital signal. Front end/receiver 2 may also include an automatic gain control circuit for automatically controlling the gain of a given signal.

Signal processing unit 6 is configured for signal processing that contains the spectrum sensing algorithms discussed in greater detail further below. Processing unit 6 may include a computer program stored on or run by hardware or software for implementing this signal processing, such as on a general purpose processor. The hardware may, for instance, be or include one or more of digital signal processing (DSP), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), and a graphics processing unit (GPU).

Display 8 is configured to display information received from unit 6. For instance, display 8 may display signals that are detected/classified (as discussed further below) and communicated to display 8. Display 8 may be any type of display known in the art, for example, a computer display/monitor, a cell phone display, a personal digital assistant (PDA) display and so forth.

Other system 10 may have any variety of configurations which may receive pertinent information from unit 6 for various purposes. Spectrum sensing provided by signal processing unit 6 to system 10 can provide value added information to system 10 for its operation in many cases. For example, other system 10 may include a cognitive software defined radio (SDR) system whereby processing unit 6 communicates to the SDR system where unused spectrum is, so that this cognitive radio system may use that unused spectrum for communications purposes. System 10 may also receive information from unit 6 for such applications as, for example, quantification of the available spectrum through spectrum observatories, on-demand spectrum survey and report, collaborative spectrum measurement and calibration, labeling of systems utilizing the spectrum, spectrum planning, spectrum mapping, coverage analysis for wireless deployment, terrain and topology—shadowing and fading analysis, complementing the database access for spectrum sharing by adding in-situ awareness and faster decision making, Space-Time-Frequency spectrum hole identification and prediction where non-time-sensitive tasks can be performed at certain times and at certain locations when the spectrum use is sparse or non-existent, and identification and geo-location of interference sources, etc.

The system and method(s) herein provide novel tunnelized second and higher-order cyclostationary processing algorithms to simultaneously detect and characterize signals. As previously noted, techniques that exploit second and higher order cyclostationary features to detect and classify signals are highly desirable. However, their pervasive use and hardware implementation have been hampered because such algorithms are highly complex, and consume a lot of energy. The inventors have discovered that severe but purposeful under-sampling of the signals through tunneling (explained herein) preserves the cyclostationarity of the signals, even when the tunnel bandwidth is much smaller than the signal bandwidth. This phenomenon is then exploited to create a low complexity and flexible suite of algorithms to simultaneously detect and classify or characterize the signals using their cyclostationary features. As demonstrated herein, such algorithms can detect and characterize signals under a highly adverse signal to interference plus noise ratio, even when multiple signals are co-channel and simultaneously interfering with each other.

In general, any spectrum sensing related processing involves some form of data collection device or spectrum sensor such as device/sensor 1. As partially discussed above, this data collection device may include one or more antennas, radio frequency (RF) receivers that tune to a particular frequency, followed by amplifiers 3 and mixers 5 to amplify and down-convert the signal for processing, and finally analog to digital (A/D) converters 7 that sample the analog signal at a nominal sampling frequency and digitize them for processing. This processing that happens in general for any spectrum sensor is well understood. The focus of our invention is the processing that happens on this digitized data to further understand what kind of signals are actually operating in the analysis bandwidth, as well as a system, apparatus or device/sensor 1 which is configured to effect this processing/the method(s) described herein. The analysis bandwidth is defined as the total bandwidth which needs to be analyzed by the spectrum sensing algorithms at a time. Typically, the analysis bandwidth is less than or equal to the sampling rate (fs) to meet the Nyquist criteria. Device/sensor 1 may be used to effect method(s) herein which may include under sampling an input signal to take samples in one or more tunnels each with a tunnel bandwidth that is equal to or less than a total analysis bandwidth of the input signal; detecting one or more cyclostationary features of the input signal based on the samples; and determining one or more signal types of the one or more cyclostationary features based on the one or more cyclostationary features.

FIG. 2 illustrates an embodiment of the concept of “tunneling”, which is purposeful under sampling of the analysis bandwidth spectrum into smaller sub-bands called tunnels. These tunnels could be a result of a simple channelizer circuit, or of something more sophisticated such as compressive sensing or sampling. In particular, FIG. 2 shows the power spectral density (PSD) for four wideband signals with no spectral overlap. The four wideband signals shown as examples are respectively Global System for Mobile (GSM), Direct sequence spread spectrum (DSSS), frequency hopping and direct sequence spread spectrum (FH-DSSS) or (FH/DS SS), and orthogonal frequency division multiplexing (OFDM).

The invention provides computationally efficient cyclostationary detection and classification algorithms which do not require processing of full signal bandwidth. As shown in FIG. 2, we formulated this novel approach called “tunneling” where we discovered that severely under-sampled signals (although distorted) retain exploitable cyclostationary properties where these tunnelized signals can be detected and characterized with the same performance as traditional cyclostationary algorithms. The exploitable cycle frequencies (CFs) in spite of tunneling are manifested as a result of repeating features of the signal that originate from the structures associated with frame size, packet size, preamble structure, pilot patterns and hop patterns, as well as fast moving features that are associated with symbol rate or chip rate. FIGS. 3A and 3B describe the signal processing flow for the tunnelized cyclostationary processing to detect known and unknown signal types.

The ability of the tunnelized cyclostationary algorithms to provide energy savings of greater than ten times that of conventional cyclostationary algorithms comes from the fact that instead of analyzing a high sample rate input signal stream, one can now process a severely sub-sampled tunnelized signal stream and apply striding, where not all the tunnels are analyzed, but instead many of them are skipped at no penalty in performance.

FIGS. 3A and 3B show the signal processing flow of the Cognitive radio Low-energy signal Analysis Sensor Integrated Circuit (CLASIC) algorithms. The CLASIC algorithms are implemented in five stages such that the sophistication and complexity of the algorithms increase with each stage. The various stages are labeled in FIG. 3B and shown respectively at blocks 12, 14, 16, 18 and 20 in FIG. 3A. In Stage 1 (block 12), channelization or tunneling is conducted, and an exhaustive spectrum survey is also conducted to detect (blind detection) all types of signals (narrowband and wideband) that are well above the noise floor. In Stage 2 (block 14), narrow-band signals are detected and characterized using second and higher order cyclostationary processing. In Stage 3 (block 16), known types of signals are detected and characterized using their second order cyclostationary properties. This may involve semi-blind low-SNR wideband signal detection, separation and classification based on second and higher order cyclostationary analysis. Stage 4 (block 18) of the process is used to detect unknown types of signals and find the detected frequency interval (DFI) which is an approximate frequency support of the signal. This may involve blind wideband signal detection and second order cyclostationary analysis. Stage 4 results are passed on to Stage 5 (block 20), which serves to detect and characterize the unknown signal type using second and higher order cyclic cumulants (HOCCs). This may involve blind wideband signal separation and classification based on second and higher order cyclostationary analysis.

Stage 1—Tunneling the Input Sample Stream

The function of Stage 1 is to perform tunneling of the input data stream. In Stage 1, device/sensor 1 may be used to under sample an input signal to take samples in one or more tunnels each with a tunnel bandwidth that is equal to or less than a total analysis bandwidth of the input signal. A channelizer may be used with a sufficiently long window to create enough isolation between tunnels to be able to withstand interference from other higher power signals, which may be signals with highly dynamic characteristics. For example, as shown in FIG. 3D, the channelizer 32 may be a polyphase windowed discrete Fourier transform (DFT) channelizer. The polyphase windowed DFT channelizer may be followed by an energy detection stage 34 performed by applying a block averaging circuit to each tunnel over time to obtain accurate estimates of the noise floor of the given tunnel and hence detect signals that are sufficiently above the noise floor to perform rapid analysis of the RF scene. This is equivalent to performing Energy Detection (ED) for the spectrum, with frequency resolution equal to the bandwidth of the given tunnel. Finally, tunnelized signals are provided to a whitener circuit, which performs the function of creating a level playing field for all signals and normalizes it to an average amplitude of unity, no matter what their input received power may be, which allows signals with a wide variety of cyclostationary properties to be detected without causing false alarms or missed detection for other signals. The other advantage of the whitener is to scale the signal without losing any information which ensures that the maximum or the minimum bit-widths for digital signal processing (DSP) in hardware implementation do not go out of proportion, even when the signal suffers from excessive low or high swings. If this step is skipped, then it is difficult to set a particular threshold when a wide variety of signals need to be analyzed.

Stage 3—Simultaneous Detection and Characterization of Known Type of Signals

FIG. 3C is a flow diagram showing Stage 3 spectral correlation function estimation for known type signal types using their know or previously estimated cycle frequencies. The function of Stage 3 is to perform detection and characterization of known types of signals using their known or previously estimated cycle frequencies (CFs). The primary algorithm that is used to determine the cyclostationary property of the signals is given by the well-known Equations (1) below that represent the cyclic periodogram for the p^(th) tunnel and processing of N samples for a given CF α, and for the frequency support f. Xp and Xq correspond to the discrete Fourier transforms (DFT) of the whitened signals for the p^(th) tunnel and the q^(th) tunnel respectively. X _(p,N)(k,f)=FFTN(x _(p)(t−kN)) FFTN( ) denotes N point Discrete Fourier Transform (DFT) processing. X_(p,N) and X_(q,N) correspond to the p^(th) and q^(th) tunnel signals after DFT processing. Parameter α is cycle frequency, parameter f is support frequency and parameter M is the number of blocks with each block containing N point DFT output. I_(x) _(p) _(x) _(q) ^(α) is the general spectral correction function with cycle frequency α and is obtained by p^(th) and q^(th) tunnel signals. S_(x) _(p) _(x) _(q) ^(α) is the spectral correction function after time smoothing by averaging M coherent cyclic periodograms.

$\begin{matrix} {{{I_{x_{p}x_{q}}^{\alpha}\left( {k,f} \right)} = {{X_{p,N}\left( {k,{f + \frac{\alpha}{2}}} \right)}{X_{q,N}^{*}\left( {k,{f - \frac{\alpha}{2}}} \right)}}}{{{\hat{S}}_{x_{p}x_{q}}^{\alpha}(f)} = {\frac{1}{NM}{\sum\limits_{k = 0}^{M - 1}{{I_{x_{p}x_{q}}^{\alpha}\left( {k,f} \right)}e^{{- i}\; 2{\pi\alpha}\;{kN}}}}}}} & {{Equations}\mspace{14mu} 1} \end{matrix}$ The cyclic periodogram, when averaged over many data records, with a valid phase factor, produces an estimate of the spectral correlation function (SCF) which has the (f,α) support (also known as the principal domain) that resembles a diamond.

FIG. 4A shows an illustration of the SCF of an entire or total analysis bandwidth. With reference to FIG. 4A, it is noted that all signals and noise contribute to the power spectrum (α=0), but different signals contribute to different parts of the spectral correlation planes. What is difficult to detect and sort in the power spectral density (PSD) can be easy in the planes. Signal parameters reflected in spectral correlation can be estimated in the presence of interference.

FIG. 4B illustrates how we perform our Stage 3 processing to only detect certain known CFs for the signals that we wish to detect. As shown in FIGS. 4B and 4C, if the CF of a signal is known a priori, then it is not necessary to compute the SCF exhaustively. Rather, one can significantly reduce the complexity by simply looking for the SCF response for the known CFs and comparing the response against a threshold for noise and other signal types. This results in significant reduction in computational cycles involved. Further, as mentioned below, not all tunnels need to be analyzed for signals. Depending upon the size of the tunnels and the signals that need to be detected, we apply a striding factor, which allows a significant number of tunnels to be skipped without any performance penalty.

Three types of cyclostationary statistics are computed in Stage 3. “Basic” tunnel response corresponds to the processing where the CFs to be found are smaller than the tunnel bandwidth. Hence, both the Xp and the Xq terms in Equation (1) come from the complex baseband samples of the same tunnel, or in other words, p=q. As an example, a Wideband Code Division Multiple Access (WCDMA) signal contains strong CFs at 15 kHz, 30 kHz and 45 kHz, which in our case belongs to Basic tunnel processing. “Dual” tunnel response corresponds to the processing where the CFs to be found are greater than the tunnel bandwidth. Hence, both the Xp and the Xq terms in Equations (1) come from the complex baseband samples of two different tunnels, where the distance between the two tunnels is roughly equal to the CF that needs to be detected. As an example, a WCDMA signal that contains strong CFs at 3.8488 MHz would belong to Dual tunnel processing.

Both the Basic and Dual tunnel responses correspond to a non-conjugate cyclic periodogram, where a conjugate operation is applied for the Xq term in Equation (1). However, when this conjugate term is missed, and the CF to be detected is greater than the tunnel bandwidth, then it results in the statistic that is referred to as the “Dual Conjugate” as shown in Equation 2:

$\begin{matrix} {{{\hat{I}}_{x_{p}x_{q}^{*}}^{\alpha}\left( {t,f} \right)} = {\frac{1}{N}{X_{p,N}\left( {t,{f + \frac{\alpha}{2}}} \right)}{X_{q,N}\left( {t,{\frac{\alpha}{2} - f}} \right)}}} & {{Equation}\mspace{14mu} 2} \end{matrix}$

The dual conjugate is used to detect signals that have no known non-conjugate CFs (e.g., Advanced Television Standards Committee Digital Television or ATSC DTV signal), but contain CFs that are proportional to their carrier frequency and the symbol rate. As can be seen from Equation 2, not applying the conjugation factor retains the double carrier terms in the SCF.

FIGS. 5A, 5B, 5C and 5D show one of the extraordinary results of the system and process presented herein. This is the case where five signals {viz. Global System for Mobile (GSM), Wideband Code Division Multiple Access (WCDMA), unknown type, Advanced Television Standards Committee—Digital Television (ATSC-DTV), 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) (5 MHz)} are co-channel and interfering with each other, as shown in FIG. 5A. Using Basic tunnel processing (FIG. 5B), Dual tunnel processing (FIG. 5C) and Dual Conjugate tunnel processing (FIG. 5D), it is possible to detect and classify all the known type signals in spite of them being right on top of each other and in extremely poor signal to interference plus noise ratio (SINR) environment. The FIG. 5B graph illustrates basic tunnel based detection and classification, and more particularly shows detection and classification of GSM, WCDMA and LTE5M using basic tunneling; the FIG. 5C graph illustrates dual tunnel based detection and classification, and more particularly shows detection and classification of WCDMA using dual tunneling; and the FIG. 5D graph illustrates dual conjugate tunnel based detection and classification, and more particularly shows detection and classification of ATSC-DTV using dual conjugate tunneling.

Table I illustrates the confusion matrix for the scene represented in FIGS. 5A-5D for 100 trials, where each signal had an in-band signal to noise ratio (SNR) of 3 dB. As can be seen from Table I, the proposed tunnelized cyclostationary algorithms are able to detect and characterize the signals with nearly ideal performance.

TABLE 1 STAGE 3 DETECTION AND CHARACTERIZATION - CONFUSION Classified As: In-band LTE SNR = 3 dB WCDMA GSM (5 MHz) DTV Input WCDMA 100% Signal GSM 100% LTE 100% (5 MHz) DTV 100%

Some other things of note are that, during experimentation, the inventors were able to create significant reduction in computational footprint by using partial fast Fourier transform (FFT) points for Signals Xp and Xq in Equations (1) and (2). As an example, the FFT size may be 16, the analysis bandwidth may be 50 MHz which when divided into 256 tunnels results in each tunnel having a bandwidth of 195.3125 kHz each. The spectrum sensing time for the results shown in FIGS. 5A-D and Table I was 160 milliseconds (ms), or approximately 2²³ samples if the sampling rate (fs) is 50 MHz.

IV. Stage 4—Unknown Signal Detection

Stage 4 includes unknown type of signal detection and detected frequency interval (DFI) computation. This is accomplished using both cyclostationary processing and energy detection. Since it is not possible to know the characteristics of the unknown signal, an exhaustive CF search for the tunnels is performed. The criteria used is to detect at least one CF in a given tunnel. FIG. 6A illustrates the concept of Stage 4 unknown signal type detection where tunnels with similar CFs are grouped together to create a DFI, which is then provided to the Stage 5 processing for further analysis.

Computing the spectral correlation function (SCF) using Equations (1) and (2) is efficient when the CFs are known a priori, but not very efficient when one does not know the CFs, as disclosed by W. A. Brown and H. H. Loomis, Jr., in Digital Implementations of Spectral Correlation Analyzers, IEEE Transactions on Signal Processing, Vol. 41, No. 2, February 1993, pp. 703-720.

The below-noted algorithm, which is extremely hardware friendly, divides the 2-D (f,α) plane into diagonally arranged strips as shown in FIG. 6B, which for instance shows a strip 6 and a strip 8 (of eight strips into which the plane might, for example, be divided). Once again, it is not necessary to analyze each and every strip in each and every tunnel. In the majority of cases, a few strips are enough to detect the signal with reasonable probability of detection (Pd) and probability of false alarm (Pfa).

The generic strip spectral correlation analyzer (SSCA) algorithm 38 may be described using the following Equations (3):

$\begin{matrix} {{{{\hat{S}}_{x_{N}}^{\alpha}(f)} = {\sum\limits_{r = 0}^{N - 1}{{X_{N\;\prime}\left( {r,{f + \frac{\alpha}{2}}} \right)}{x^{*}(r)}e^{{- i}\; 2\pi\;{r{({\frac{\alpha}{2} - f})}}}}}}{{X_{N\;\prime}\left( {r,f} \right)} = {\sum\limits_{j = {{- N}/2}}^{\frac{N\;\prime}{2} - 1}{{\alpha(j)}{x\left( {r - j} \right)}e^{{- i}\; 2\pi\;{f{({r - j})}}}}}}} & {{Equations}\mspace{14mu}(3)} \end{matrix}$ where two fast Fourier transforms (FFTs) of respective sizes N′ and N (i.e., a first size FFT N′ and a second size FFT N) are used or defined, wherein N is the data length or spectrum sensing length, and N′ is a smaller than N and may be much smaller than N. Ŝ_(x) _(p) _(x) _(q) ^(α) is a strip spectral correction function obtained by N samples. α is cycle frequency and f is support frequency. x(r) is a time domain signal being processed and a(⋅) are the coefficients of data-tapering window respectively. FFT N′ may also be called a channelizer. All this results in a comprehensive computation of the spectral correlation function over the (f,α) principal domain. FIG. 6A shows the implementation of the SSCA using N′ point and N point FFTs. Two input sample streams are provided to the SSCA. If the sample streams originate from the same tunnel, i.e., when k=p, the resultant SSCA is termed as Auto SSCA. Referring to FIG. 3E, when kth and pth whitener outputs 36 to the SSCA 38 come from two different tunnels, then the resulting SSCA 38 is termed as the Cross SSCA. Stage 4 may use Auto or Cross SSCA. Stage 5 uses both the Auto and the Cross SSCA computation. For example, N′ may be 8, N may be 2¹⁵ or approximately 32,000 samples, which corresponds to approximately 160 ms of sensing at 50 MHz of sampling and 256 tunnels.

Referring to FIG. 6A, a regularly spaced set of tunnels may be used with S>1. For each visited tunnel, the full non-conjugate SCF may be computed using the SSCA algorithm. Significant CFs may be detected using the spectral coherence statistic. Spectral bands containing non-noise energy may be identified by grouping together adjacent (modulo striding) tunnels containing CFs. The signal at 28 in FIG. 6A will be detected, whereas the signal at 30 in FIG. 6A will be missed. FIG. 6A shows at 32 intervals which may be blindly estimated. To blindly estimate cycle frequencies that indicate the presence of non-noise RF energy, spectral correlation analysis is applied to a set of sparsely sampled tunnels, resulting in the subdiamonds near the frequency axis in the left plot of FIG. 6A. The SSCA is used to exhaustively analyze all the cycle frequencies that can appear in each subdiamond shown in the left plot of FIG. 6A.

FIGS. 7A, 7B and 7C illustrate the performance of the Stage 4 processing using Basic or Auto SSCA processing for a single strip. The graph in FIG. 7A illustrates the input scene with an unknown signal type. The graph in FIG. 7B illustrates the number of new CFs detected for the tunnels occupied by the unknown signal type, and more particularly shows at 22 detected frequency intervals (DFI) with similar CF content, and at 24 that ATSC video carrier and GSM show very strong CF content. The graph in FIG. 7C illustrates the DFI region, which is the region that is further subjected to Stage 5 processing for further analysis. The graph in FIG. 7C shows at 26 the unknown signal and its region of support detected using Stage 4 processing.

Stage 5—Unknown Signal Characterization using Second and Higher Order Cyclic Cumulants

Stage 5 is the most complicated stage of our CLASIC circuit since it needs to process the DFI regions for unknown signal types provided by Stage 4, and not only characterize the signal type, but also identify its modulation. Hence, the Stage 5 processing is broken down into four levels (0, 1, 2 and 3) as shown in FIG. 8A. Although Stages 1 through 4 as well as Stage 5 Level 0 and 1 are processed after tunneling, it is not necessary to perform tunneling for Stage 5 Level 2 and 3. Stage 4 provides the estimate of the carrier frequency and the DFI of the detected signals to Stage 5, where the Second and Higher Order analysis is then performed on a portion of the band that is known to contain the signal.

FIG. 8A illustrates the details of Stage 5 Levels 0 through 3 and how these Levels are used to provide signal characteristics understanding from the lowest fidelity to the highest fidelity. FIG. 8B shows the process of further analyzing the DFI by analyzing the principal domain by dividing it into smaller diamonds or subdiamonds or subdiamond regions (here, 49 subdiamonds). FIG. 8B illustrates that one way to obtain the CF pattern is exhaustive estimation of SCF using only tunnels that span the DFI. If all subdiamonds are analyzed by performing cross spectral correlation analysis for all possible pairs of tunnels, the subdiamonds will tile the DFI's principal domain, as shown in the left plot or graph of FIG. 8B. A similar analysis can be performed for the conjugate spectral correlation function, again using all possible tunnel pairs, as illustrated in the right plot or graph of FIG. 8B.

FIG. 8C illustrates that not all the subdiamond regions need to be analyzed for blind classification of the unknown types of signal. Thus, a selected subset of tunnels may be analyzed to infer the CF pattern. For example, the non-conjugate spectral correlation graph on the left of FIG. 8C shows that 19 of the 49 subdiamonds of FIG. 8B may be analyzed for this purpose, and the conjugate spectral correlation graph on the right of FIG. 8C shows that 15 of the 49 subdiamonds of FIG. 8B may likewise be analyzed. Use of only some tunnel pairs in the cross spectral correlation analysis can still provide sufficient information to detect and classify most RF signals. FIG. 8C shows a particular sampling of all possible tunnel pairs for both the non-conjugate and conjugate spectral correlation function. This particular sampling places subdiamonds in regions near the frequency and cycle frequency axes, which are the regions mostly likely to contain important cycle frequencies.

Stage 5 Levels 0 and 1 correspond to characterizing the unknown type of signal utilizing exhaustive second order SCF analysis using SSCA. Unlike Stage 4, this analysis is much more exhaustive and involves not only computing all the CFs, but also their patterns. The SCF for any CF in the original high-rate data can be estimated from tunnels by using appropriate auto or cross SCF estimators. Auto non-conjugate SCF estimates cover the CFs near the frequency axis. Auto conjugate SCF estimates cover the CFs near the CF axis. Cross SCF estimates fill in the remainder of the two diamonds. This has been illustrated using FIG. 4C.

The large diamond on the left of FIG. 4C illustrates the wideband data scenario; this is the aerial view of the spectral correlation function for the wideband data prior to tunneling. The lines inside the diamond indicate cycle frequencies for various signals. The cycle frequencies that are known in advance of processing are shown by dashed lines. The large diamond on the right of FIG. 4C illustrates basic and dual tunneling. Each subdiamond on the plot represents the spectral correlation function that can be estimated by performing a cross spectral correlation analysis between two tunnels. If all pairs are considered, the large diamond is tiled by the smaller diamonds. The indices inside the subdiamonds indicate the involved tunnels, assuming that there are eight tunnels total. A single number indicates that the cross operation defaults to an auto operation. The Basic Tunneling employs only tunnel pairs that result in the subdiamonds near the frequency axis; this applies to all known cycle frequencies that have magnitude less than the tunnel bandwidth. On the other hand, Dual Tunneling employs other pairs of tunnels, resulting in the subdiamonds shown well above the frequency axis.

Stage 5 Levels 2 and 3 conduct further analysis to separate the modulations of various signal types. For example, as shown in FIG. 8A, 16-QAM (quadrature amplitude modulation) and π/4 DQPSK (differential quadrature phaseshift keying) have identical second order cyclostationary features, so they cannot be distinguished based on second order statistics alone. However, their higher order features, computed from the higher order cyclic cumulants (HOCC) show remarkable difference in their patterns. Hence, the HOCCs can be effectively used to distinguish between the modulations of various unknown types of signals.

What makes the Stage 5 implementation complex is the fact that HOCCs of up to the sixth order may need to be computed. The order implies the number of terms to be multiplied. For example, a Sixth Order term is obtained by multiplying the signal six times. The HOCCs are then synthesized using various combinations of these n^(th) order terms where the constituent signals may or may not be conjugated. The basic equation for the higher order cyclic temporal moment function (CTMF) is given by Equation (4).

$\begin{matrix} {{{\hat{R}}_{x_{d}}^{\alpha}\left( {\tau,n,m} \right)} = {\frac{1}{2K}{\sum\limits_{k = {- K}}^{K - 1}{\left\lbrack {\prod\limits_{j = 1}^{n}{x_{d}^{{(*})_{j}}\left( {k + \tau_{j}} \right)}} \right\rbrack e^{{- i}\; 2{\pi\alpha}\; k}}}}} & {{Equation}\mspace{14mu}(4)} \end{matrix}$

Here, α is the CF of the higher order cyclic moment function, x_(d) is the detected unknown signal, τ and τ_(j) are the lag factors, n is the order and m terms are conjugated. α is cycle frequency and x_(d) is a time domain signal being processed. The symbol (*) denotes whether the complex term is conjugated or not, and τ is time offset. The HOCCs are derived from the CTMFs. The derivation becomes significantly complicated due to tunneling, where the signal x_(d) is then decomposed into its tunneled components.

FIG. 9A shows Stage 5 Level 0 and Level 1 Cycle Frequency Pattern analysis to recognized various modulation formats. The Signal to Noise Ratio (SNR) is 3 dB in this illustration. FIGS. 9B and 9C show Stage 5 higher order cyclic cumulants (HOCC) for modulation recognition where second order signal processing may not be sufficient.

FIG. 9A shows that floating point and fixed point implementations created by the inventors match well at in-band SNR of −1 dB and we are able to classify the signal accurately as a binary phase-shift keying (BPSK) signal. Once again, ways to significantly reduce the computational complexity have been found by only processing a subset of strips for each tunnel. One other important aspect that was not highlighted earlier is the fact that Stage 5 processing involves not only the auto-SSCA, where the elements for second order SCF come from the same tunnel, but also the cross-SSCA, where the element of the SCF come from different tunnels to find CFs that are greater than the tunnel bandwidth.

FIGS. 9B and 9C show Stage 5 higher order cyclic cumulants (HOCC) for modulation recognition where second order signal processing may not be sufficient. HOCCs, in general require extensive computations and are used when second order cyclostationary statistics are not sufficient to discriminate between signals. An example of this is shown in FIG. 9B, where the second order cyclostationary statistics for 16-Quadrature Amplitude Modulation (QAM) and p/4 Differential Quadrature Phase Shift Keying (DQPSK) signals have the same patterns. As a result of this, it becomes nearly impossible to discriminate between these signals based on just the second order cyclostatioary statistics. Similarly, various types of M-QAM (e. g. 4-QAM, 16-QAM, 32-QAM, 64-QAM) cannot be distinguished using the second order cyclostationary statistics alone.

FIG. 9C shows the HOCC patterns for 16-QAM and p/4-DQPSK signals. The Y-axis in the figure represents the harmonic number. The Zero line on the Y axis is the direct current (d.c.) component. +/−1 represents the HOCCs corresponding to harmonics of the signal carrier frequency+/−fc. +/−2 represents the HOCCs corresponding to the harmonics of two times the signal carrier frequency (+/−2fc) etc. The x-axis corresponds to the orders of the HOCCs. The variable ‘n’ refers to the order of the statistic. The term ‘m’ refers to the number of terms that have been conjugated. For example, (2,0) refers to the second order statistic with no terms conjugated and (2,1) refers to second order statistic with one term conjugated. The patterns correspond to the statistics that show magnitude above a certain set threshold. Based on the HOCC patterns, one can see that it is possible to distinguish between 16-QAM and p/4-DQPSK modulation types. According to one embodiment of the present invention, a tunnelized version of the HOCCs was used to determine various modulations. In a degenerate case, the tunnel size may be equal to the width of the signal bandwidth.

Those skilled in the art will appreciate that we have provided novel tunnelized second and higher-order cyclostationary processing algorithms to simultaneously detect and characterize signals. Our algorithms show that severe but purposeful under-sampling of the signals through tunneling preserve the cyclostationarity of the signals, even when the tunnel bandwidth is much smaller than the signal bandwidth. This phenomenon is exploited to create low complexity and flexible suite of algorithms to simultaneously detect and characterize the signals using their cyclostationary features. It has also been demonstrated that such algorithms can detect and characterize signals under a highly adverse signal to interference plus noise ratio, even when multiple signals are co-channel and simultaneously interfering with each other.

In the foregoing description, certain terms have been used for brevity, clearness, and understanding. No unnecessary limitations are to be implied therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes and are intended to be broadly construed. Therefore, the invention is not limited to the specific details, the representative embodiments, and illustrative examples shown and described. Thus, this application is intended to embrace alterations, modifications, and variations that fall within the scope of the appended claims. 

What is claimed is:
 1. A spectrum sensor, comprising: a processing unit for processing digital signals representative of radio frequency signals processed by a radio frequency front end, wherein the processing unit comprises at least one processor and a memory section having instructions stored thereon that when executed directs the at least one processor to execute tunnelized second and higher-order cyclostationary instructions that process the digital signals over an analysis bandwidth to determine one or more signal types based on one or more second and higher order cyclostationary features and under sample the digital signals to generate one or more tunnels that have a respective tunnel bandwidth, wherein the tunnel bandwidth is less than or equal to the analysis bandwidth.
 2. The spectrum sensor according to claim 1, wherein the radio frequency front end comprises, at least one amplifier, and at least one mixer, configured to receive the radio frequency signals and process the radio frequency signals into downconverted signals from at least one antenna.
 3. The spectrum sensor according to claim 2, further comprising an analog to digital converter that processes the downconverted signals into the digital signals.
 4. The spectrum sensor according to claim 1, wherein the tunnelized second and higher-order cyclostationary instructions perform the following: identify tunnelized signal samples from the one or more tunnels; whiten the tunnelized signal samples to generate tunnelized and whitened signal samples; produce a spectral correlation function of the tunnelized and whitened signal samples; and detect the second and higher order cyclostationary features of the radio frequency signals by analyzing the spectral correlation function.
 5. The spectrum sensor according to claim 1, wherein the processing unit is communicatively coupled to an other system and provides the one or more signal types to the other system, wherein the other system processes the one or more signal types to provide at least one of: quantification of available spectrum, on-demand spectrum survey, spectrum measurement and calibration, labeling of spectrum systems, spectrum planning, spectrum mapping, coverage analysis for wireless deployment, terrain and topology shadowing, fading analysis, space-time-frequency spectrum hole identification, and identification and geo-location of interference sources.
 6. The spectrum sensor according to claim 1, wherein the one or more signal types are sent to a display, wherein the display is a computer display, a cell phone display, or a personal digital assistant (PDA) display.
 7. The spectrum sensor according to claim 1, wherein the one or more signal types are selected from Direct Television (DTV), Global System for Mobile (GSM), Wideband Code Division Multiple Access (WCDMA), and Long Term Evolution (LTE).
 8. The spectrum sensor according to claim 1, wherein the processing unit further comprises series or parallel processing of a signal detection algorithm, a narrow band blind signal classification algorithm, a non-blind signal detection algorithm, a blind frequency interval detection algorithm and a blind signal classification algorithm.
 9. The spectrum sensor according to claim 1, wherein the processing unit further comprises a channelizer configured to under sample the digital signals.
 10. The spectrum sensor according to claim 1, wherein the processing unit utilizes a Strip Spectral Correlation Analyzer (SSCA) algorithm that is applied to the one or more tunnels over the analysis bandwidth.
 11. A spectrum sensing device, comprising: at least one antenna; a radio frequency front end coupled to the antenna and receiving radio frequency signals, wherein the radio frequency front end tunes to a frequency band and produces a downconverted signal; an analog to digital converter for converting the downconverted signal to digitized data; a processing unit for processing the digitized data, the processing unit comprising at least one processor and instructions that when executed by the at least one processor execute tunnelized second and higher-order cyclostationary instructions to determine one or more signal types based on one or more second and higher order cyclostationary features; and a display for displaying the one or more signal types, wherein the radio frequency front end collects signals over an analysis bandwidth, the tunnelized second and higher-order cyclostationary instructions comprise under sampling in one or more tunnels having a respective tunnel bandwidth, and wherein the tunnel bandwidth is less than or equal to the analysis bandwidth.
 12. The spectrum sensing device according to claim 11, wherein the display is a computer display, a cell phone display, or a personal digital assistant (PDA) display.
 13. The spectrum sensing device according to claim 11, wherein the processing unit is configured to: identify tunnelized signal samples from the one or more tunnels; whiten the tunnelized signal samples to generate tunnelized and whitened signal samples; produce a spectral correlation function of the tunnelized and whitened signal samples; and detect the second and higher order cyclostationary features by analyzing the spectral correlation function.
 14. The spectrum sensing device according to claim 11, wherein the processing unit is communicatively coupled to an other system and provides the one or more signal types to the other system, wherein the other system processes the one or more signal types to provide at least one of: quantification of available spectrum, on-demand spectrum survey, spectrum measurement and calibration, labeling of spectrum systems, spectrum planning, spectrum mapping, coverage analysis for wireless deployment, terrain and topology shadowing, fading analysis, space-time-frequency spectrum hole identification, and identification and geo-location of interference sources.
 15. A non-transitory computer readable medium storing instructions executable by a processing device, the instructions causing the processing device to classify radio frequency signals by: under sampling an input signal to generate one or more tunnels; identifying tunnelized signal samples from the one or more tunnels, wherein the tunnelized signal samples have a bandwidth that is equal to or less than an analysis bandwidth of the radio frequency signals; whitening the tunnelized signal samples resulting in tunnelized and whitened signal samples; producing a spectral correlation function of the tunnelized and whitened signal samples; and detecting a set of second and higher order cyclostationary features of the radio frequency signals by analyzing the spectral correlation function.
 16. The non-transitory computer readable medium according to claim 15, wherein the execution of the instructions further causes the processing device to determine one or more signal types from the set of second and higher order cyclostationary features.
 17. The non-transitory computer readable medium according to claim 16, wherein the processing device is communicatively coupled to an other system and provides the one or more signal types or the set of second and higher order cyclostationary features to the other system, wherein the other system processes the one or more signal types to provide at least one of: quantification of available spectrum, on-demand spectrum survey, spectrum measurement and calibration, spectrum labeling of systems, spectrum planning, spectrum mapping, coverage analysis for wireless deployment, terrain and topology shadowing, fading analysis, space-time-frequency spectrum hole identification, and identification and geo-location of interference sources.
 18. The non-transitory computer readable medium according to claim 16, wherein the one or more signal types are displayed on a display.
 19. The non-transitory computer readable medium according to claim 16, further comprising: transmitting a signal based on the one or more signal types. 